{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pickle\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tf.logging.set_verbosity(tf.logging.INFO)\n",
    "\n",
    "def cnn_model(features, labels, mode, config):\n",
    "    #data format: (index_in_batch, height, width, channel)\n",
    "    # mode: Indicate it is in training or testing or predicting\n",
    "\n",
    "    shape = np.append([-1], features.shape[1:])\n",
    "    height, width = features.shape[1], features.shape[2]\n",
    "    convKernel = [3, 3]\n",
    "    stridesSize = (1, 1)\n",
    "    poolSize = [2, 2]\n",
    "    dropout = 0.2\n",
    "\n",
    "    # Input reshape\n",
    "    input_layer = tf.cast(tf.reshape(features, shape), tf.float32)\n",
    "\n",
    "    #Convolutional layer 1\n",
    "    conv1 = tf.layers.conv2d(\n",
    "            inputs = input_layer, filters = 8, kernel_size = convKernel, \n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"conv1\")\n",
    "    \n",
    "    #Convolutional layer 2\n",
    "    conv2 = tf.layers.conv2d(\n",
    "            inputs = conv1, filters = 16, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"conv2\")\n",
    "\n",
    "    #Pooling\n",
    "    pool1 = tf.layers.max_pooling2d(\n",
    "            inputs = conv2, pool_size = poolSize, strides = 2, name = \"pooling1\")\n",
    "\n",
    "    #Convolutional layer 3\n",
    "    conv3 = tf.layers.conv2d(\n",
    "            inputs = pool1, filters = 16, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"conv3\")\n",
    "\n",
    "    dropout1 = tf.layers.dropout(inputs = conv3, rate = dropout, training = (mode == tf.estimator.ModeKeys.TRAIN))\n",
    "\n",
    "    #Convolutional layer 4\n",
    "    conv4 = tf.layers.conv2d(\n",
    "            inputs = dropout1, filters = 32, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"conv4\")\n",
    "\n",
    "    dropout2 = tf.layers.dropout(inputs = conv4, rate = dropout, training = (mode == tf.estimator.ModeKeys.TRAIN))\n",
    "\n",
    "    #Convolutional layer 5\n",
    "    conv5 = tf.layers.conv2d(\n",
    "            inputs = dropout2, filters = 32, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"conv5\")\n",
    "\n",
    "    dropout3 = tf.layers.dropout(inputs = conv5, rate = dropout, training = (mode == tf.estimator.ModeKeys.TRAIN))\n",
    "\n",
    "    pool2 = tf.layers.max_pooling2d(\n",
    "            inputs = dropout3, pool_size = poolSize, strides = 2, name = \"pooling2\")\n",
    "\n",
    "    #Convolutional layer 6\n",
    "    conv6 = tf.layers.conv2d(\n",
    "            inputs = pool2, filters = 64, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"conv6\")\n",
    "\n",
    "    dropout3 = tf.layers.dropout(inputs = conv6, rate = dropout, training = (mode == tf.estimator.ModeKeys.TRAIN))\n",
    "\n",
    "    #Convolutional layer 7\n",
    "    conv7 = tf.layers.conv2d(\n",
    "            inputs = dropout3, filters = 64, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"conv7\")\n",
    "\n",
    "    dropout4 = tf.layers.dropout(inputs = conv7, rate = dropout, training = (mode == tf.estimator.ModeKeys.TRAIN))\n",
    "\n",
    "    pool3 = tf.layers.max_pooling2d(\n",
    "            inputs = dropout4, pool_size = poolSize, strides = 2, name = \"pooling3\")\n",
    "\n",
    "    [_, height, width, channel] = pool3.get_shape().as_list()\n",
    "\n",
    "    unsample1 = tf.image.resize_nearest_neighbor(\n",
    "            images = pool3, size = ( height * 2, width * 2), name = \"unsampling1\")\n",
    "    \n",
    "    #Deconvolution layer 1\n",
    "    deconv1 = tf.layers.conv2d_transpose(\n",
    "            inputs = unsample1, filters = 64, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"deconv1\")\n",
    "\n",
    "    dropout5 = tf.layers.dropout(inputs = deconv1, rate = dropout, training = (mode == tf.estimator.ModeKeys.TRAIN))\n",
    "\n",
    "    #Deconvolution layer 2\n",
    "    deconv2 = tf.layers.conv2d_transpose(\n",
    "            inputs = dropout5, filters = 64, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"deconv2\")\n",
    "\n",
    "    dropout5 = tf.layers.dropout(inputs = deconv2, rate = dropout, training = (mode == tf.estimator.ModeKeys.TRAIN))\n",
    "\n",
    "    [_, height, width, channel] = dropout5.get_shape().as_list()\n",
    "    \n",
    "    unsample2 = tf.image.resize_nearest_neighbor(\n",
    "            images = dropout5, size = (height * 2, width * 2), name = \"unsampling2\")\n",
    "\n",
    "    #Deconvolution layer 3\n",
    "    deconv3 = tf.layers.conv2d_transpose(\n",
    "            inputs = unsample2, filters = 32, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"deconv3\")\n",
    "\n",
    "    dropout6 = tf.layers.dropout(inputs = deconv3, rate = dropout, training = (mode == tf.estimator.ModeKeys.TRAIN))\n",
    "\n",
    "    #Deconvolution layer 4\n",
    "    deconv4 = tf.layers.conv2d_transpose(\n",
    "            inputs = dropout6, filters = 32, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"deconv4\")\n",
    "\n",
    "    dropout7 = tf.layers.dropout(inputs = deconv4, rate = dropout, training = (mode == tf.estimator.ModeKeys.TRAIN))\n",
    "\n",
    "    #Deconvolution layer 5\n",
    "    deconv5 = tf.layers.conv2d_transpose(\n",
    "            inputs = dropout7, filters = 16, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"deconv5\")\n",
    "\n",
    "    dropout8 = tf.layers.dropout(inputs = deconv5, rate = dropout, training = (mode == tf.estimator.ModeKeys.TRAIN))\n",
    "\n",
    "    [_, height, width, channel] = dropout8.get_shape().as_list()\n",
    "\n",
    "    unsample3 = tf.image.resize_nearest_neighbor(\n",
    "            images = dropout8, size = (height *2, width * 2), name = \"unsampling3\")\n",
    "\n",
    "    #Deconvolution layer 6\n",
    "    deconv6 = tf.layers.conv2d_transpose(\n",
    "            inputs = unsample3, filters = 16, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"deconv6\")\n",
    "\n",
    "    #Deconvolution layer 7\n",
    "    deconv7 = tf.layers.conv2d_transpose(\n",
    "            inputs = deconv6, filters = 1, kernel_size = convKernel,\n",
    "            strides = stridesSize, activation = tf.nn.relu, name = \"deconv7\")\n",
    "    \n",
    "    predictions = {\n",
    "        \"predict_lane\": deconv7\n",
    "    }\n",
    "    \n",
    "    if mode == tf.estimator.ModeKeys.PREDICT:\n",
    "        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)\n",
    "\n",
    "    # Calculate Loss (for both TRAIN and EVAL modes)\n",
    "    loss = tf.losses.mean_squared_error(predictions = deconv7, labels = labels)\n",
    "\n",
    "    # Configure the Training Op (for TRAIN mode)\n",
    "    if mode == tf.estimator.ModeKeys.TRAIN:\n",
    "        optimizer = tf.train.AdamOptimizer()\n",
    "        train_op = optimizer.minimize(\n",
    "            loss=loss,\n",
    "            global_step=tf.train.get_global_step())\n",
    "        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)\n",
    "\n",
    "      # Add evaluation metrics (for EVAL mode)\n",
    "    eval_metric_ops = { \"accuracy\": tf.metrics.mean(tf.losses.mean_squared_error(labels=labels, predictions=predictions[\"predict_lane\"]))}\n",
    "    \n",
    "    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(52857, 80, 160, 3)\n",
      "(52857, 80, 160, 1)\n",
      "42285\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_keep_checkpoint_every_n_hours': 10000, '_task_type': 'worker', '_num_ps_replicas': 0, '_master': '', '_session_config': None, '_service': None, '_model_dir': './tmp/lane_prediction', '_task_id': 0, '_num_worker_replicas': 1, '_evaluation_master': '', '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_global_id_in_cluster': 0, '_tf_random_seed': None, '_train_distribute': None, '_log_step_count_steps': 100, '_keep_checkpoint_max': 5, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f17acecd860>, '_is_chief': True, '_save_checkpoints_steps': None}\n",
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into ./tmp/lane_prediction/model.ckpt.\n",
      "INFO:tensorflow:loss = 120.65565, step = 0\n",
      "INFO:tensorflow:global_step/sec: 1.80005\n",
      "INFO:tensorflow:loss = 0.053419337, step = 100 (55.548 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.81348\n",
      "INFO:tensorflow:loss = 0.054463334, step = 200 (55.138 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.80262\n",
      "INFO:tensorflow:loss = 0.04759983, step = 300 (55.473 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.80102\n",
      "INFO:tensorflow:loss = 0.04671736, step = 400 (55.525 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.76619\n",
      "INFO:tensorflow:loss = 0.040404495, step = 500 (56.621 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.77616\n",
      "INFO:tensorflow:loss = 0.036601037, step = 600 (56.299 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.77428\n",
      "INFO:tensorflow:loss = 0.038685456, step = 700 (56.361 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.75609\n",
      "INFO:tensorflow:loss = 0.03256484, step = 800 (56.947 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.73611\n",
      "INFO:tensorflow:loss = 0.030319069, step = 900 (57.597 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.7293\n",
      "INFO:tensorflow:loss = 0.0307054, step = 1000 (57.829 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 1064 into ./tmp/lane_prediction/model.ckpt.\n",
      "INFO:tensorflow:global_step/sec: 1.66606\n",
      "INFO:tensorflow:loss = 0.029385183, step = 1100 (60.022 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.70426\n",
      "INFO:tensorflow:loss = 0.027798668, step = 1200 (58.677 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.67259\n",
      "INFO:tensorflow:loss = 0.026162744, step = 1300 (59.787 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.68082\n",
      "INFO:tensorflow:loss = 0.025181951, step = 1400 (59.495 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.65329\n",
      "INFO:tensorflow:loss = 0.026971104, step = 1500 (60.486 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.66005\n",
      "INFO:tensorflow:loss = 0.023755144, step = 1600 (60.239 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.66\n",
      "INFO:tensorflow:loss = 0.025698341, step = 1700 (60.241 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.6322\n",
      "INFO:tensorflow:loss = 0.022242106, step = 1800 (61.267 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.62537\n",
      "INFO:tensorflow:loss = 0.023705225, step = 1900 (61.524 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.64678\n",
      "INFO:tensorflow:loss = 0.020495426, step = 2000 (60.724 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 2054 into ./tmp/lane_prediction/model.ckpt.\n",
      "INFO:tensorflow:global_step/sec: 1.55244\n",
      "INFO:tensorflow:loss = 0.022622872, step = 2100 (64.416 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.64333\n",
      "INFO:tensorflow:loss = 0.022902327, step = 2200 (60.856 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.59759\n",
      "INFO:tensorflow:loss = 0.021758623, step = 2300 (62.589 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.61745\n",
      "INFO:tensorflow:loss = 0.020069031, step = 2400 (61.825 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.59888\n",
      "INFO:tensorflow:loss = 0.022208441, step = 2500 (62.547 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.57895\n",
      "INFO:tensorflow:loss = 0.021957228, step = 2600 (63.331 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.5939\n",
      "INFO:tensorflow:loss = 0.018294604, step = 2700 (62.738 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.61481\n",
      "INFO:tensorflow:loss = 0.021260807, step = 2800 (61.927 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.59831\n",
      "INFO:tensorflow:loss = 0.019975841, step = 2900 (62.566 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.59225\n",
      "INFO:tensorflow:loss = 0.020372367, step = 3000 (62.805 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 3015 into ./tmp/lane_prediction/model.ckpt.\n",
      "INFO:tensorflow:global_step/sec: 1.60001\n",
      "INFO:tensorflow:loss = 0.01975876, step = 3100 (62.499 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.57807\n",
      "INFO:tensorflow:loss = 0.020036051, step = 3200 (63.368 sec)\n",
      "INFO:tensorflow:global_step/sec: 1.62917\n",
      "INFO:tensorflow:loss = 0.018209279, step = 3300 (61.381 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 3304 into ./tmp/lane_prediction/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 0.021010492.\n",
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Starting evaluation at 2018-04-25-18:40:02\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from ./tmp/lane_prediction/model.ckpt-3304\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Finished evaluation at 2018-04-25-18:40:21\n",
      "INFO:tensorflow:Saving dict for global step 3304: accuracy = 0.019284973, global_step = 3304, loss = 0.019284973\n",
      "{'accuracy': 0.019284973, 'loss': 0.019284973, 'global_step': 3304}\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2971: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "def main(unused_args):\n",
    "    #load data\n",
    "    labels = pickle.load(open(\"road_labels.p\", \"rb\" ))\n",
    "    labels = np.array(labels)\n",
    "    labels = labels / 200 # 200 for custom-labeled data, 255 for original data\n",
    "    train_images_raw = pickle.load(open(\"road_images.p\", \"rb\" ))\n",
    "    train_images=np.array(train_images_raw)\n",
    "    print(train_images.shape)\n",
    "    print(labels.shape)\n",
    "    \n",
    "    # train data / test data = 0.8\n",
    "    rate = 0.8\n",
    "\n",
    "    # split the data\n",
    "    train_data, test_data, train_label, test_label = \\\n",
    "    train_test_split(train_images, labels, train_size = rate, test_size = 1 - rate)\n",
    "\n",
    "    print(len(train_data))\n",
    "    # Add a logger\n",
    "    # logging_hook = tf.train.LoggingTensorHook({\"accuracy\" : \"metrics_acc\"}, every_n_iter=10)\n",
    "\n",
    "    epochs = 10\n",
    "    \n",
    "    #Train\n",
    "    train_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "        x=train_data,\n",
    "        y=train_label,\n",
    "        batch_size=128,\n",
    "        num_epochs=epochs,\n",
    "        shuffle=True)\n",
    "    \n",
    "    classifier = tf.estimator.Estimator(\n",
    "        model_fn= cnn_model,\n",
    "        model_dir= \"./tmp/lane_prediction\")\n",
    "\n",
    "    classifier.train( input_fn=train_input_fn, steps=epochs*len(train_data)/128)\n",
    "    \n",
    "    #Test\n",
    "    test_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "        x = test_data,\n",
    "        y = test_label,\n",
    "        num_epochs = 1,\n",
    "        shuffle = False)\n",
    "    \n",
    "    test_result = classifier.evaluate(input_fn= test_input_fn)\n",
    "    print(test_result)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    tf.app.run(main)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_tf_random_seed': None, '_num_ps_replicas': 0, '_task_type': 'worker', '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f66b7c347f0>, '_service': None, '_train_distribute': None, '_model_dir': './tmp/lane_prediction', '_log_step_count_steps': 100, '_session_config': None, '_save_checkpoints_secs': 600, '_global_id_in_cluster': 0, '_master': '', '_save_checkpoints_steps': None, '_evaluation_master': '', '_task_id': 0, '_save_summary_steps': 100, '_num_worker_replicas': 1, '_keep_checkpoint_every_n_hours': 10000, '_keep_checkpoint_max': 5, '_is_chief': True}\n",
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from ./tmp/lane_prediction/model.ckpt-3304\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
     "ename": "ResourceExhaustedError",
     "evalue": "OOM when allocating tensor with shape[77,8,588,1638] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv1/Conv2D = Conv2D[T=DT_FLOAT, data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"VALID\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](conv1/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, conv1/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: deconv5/Shape/_241 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_214_deconv5/Shape\", tensor_type=DT_INT32, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\nCaused by op 'conv1/Conv2D', defined at:\n  File \"/usr/lib/python3.5/runpy.py\", line 184, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/usr/lib/python3.5/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 486, in start\n    self.io_loop.start()\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 127, in start\n    self.asyncio_loop.run_forever()\n  File \"/usr/lib/python3.5/asyncio/base_events.py\", line 345, in run_forever\n    self._run_once()\n  File \"/usr/lib/python3.5/asyncio/base_events.py\", line 1312, in _run_once\n    handle._run()\n  File \"/usr/lib/python3.5/asyncio/events.py\", line 125, in _run\n    self._callback(*self._args)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 117, in _handle_events\n    handler_func(fileobj, events)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/tornado/stack_context.py\", line 276, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n    self._handle_recv()\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n    callback(*args, **kwargs)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/tornado/stack_context.py\", line 276, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2903, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2963, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-2-f603c6357114>\", line 25, in <module>\n    predictions = list(classifier.predict(input_fn=predict_input_fn))\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py\", line 496, in predict\n    features, None, model_fn_lib.ModeKeys.PREDICT, self.config)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py\", line 831, in _call_model_fn\n    model_fn_results = self._model_fn(features=features, **kwargs)\n  File \"<ipython-input-1-ef488517f438>\", line 28, in cnn_model\n    strides = stridesSize, activation = tf.nn.relu, name = \"conv1\")\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/convolutional.py\", line 621, in conv2d\n    return layer.apply(inputs)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py\", line 828, in apply\n    return self.__call__(inputs, *args, **kwargs)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py\", line 717, in __call__\n    outputs = self.call(inputs, *args, **kwargs)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/convolutional.py\", line 168, in call\n    outputs = self._convolution_op(inputs, self.kernel)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py\", line 868, in __call__\n    return self.conv_op(inp, filter)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py\", line 520, in __call__\n    return self.call(inp, filter)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py\", line 204, in __call__\n    name=self.name)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_nn_ops.py\", line 956, in conv2d\n    data_format=data_format, dilations=dilations, name=name)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py\", line 3392, in create_op\n    op_def=op_def)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py\", line 1718, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[77,8,588,1638] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv1/Conv2D = Conv2D[T=DT_FLOAT, data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"VALID\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](conv1/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, conv1/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: deconv5/Shape/_241 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_214_deconv5/Shape\", tensor_type=DT_INT32, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1321\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1322\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1323\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1306\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1307\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1408\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1409\u001b[0;31m           run_metadata)\n\u001b[0m\u001b[1;32m   1410\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor with shape[77,8,588,1638] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv1/Conv2D = Conv2D[T=DT_FLOAT, data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"VALID\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](conv1/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, conv1/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: deconv5/Shape/_241 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_214_deconv5/Shape\", tensor_type=DT_INT32, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-f603c6357114>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     23\u001b[0m     shuffle=False)\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredict_input_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0mcnn_road_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'predict_lane'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcnn_road_labels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'bridge_CNN_labels.p'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, input_fn, predict_keys, hooks, checkpoint_path, yield_single_examples)\u001b[0m\n\u001b[1;32m    507\u001b[0m           hooks=all_hooks) as mon_sess:\n\u001b[1;32m    508\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmon_sess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m           \u001b[0mpreds_evaluated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmon_sess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    510\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0myield_single_examples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mpreds_evaluated\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    565\u001b[0m                           \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    566\u001b[0m                           \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 567\u001b[0;31m                           run_metadata=run_metadata)\n\u001b[0m\u001b[1;32m    568\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    569\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun_step_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1041\u001b[0m                               \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m                               \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1043\u001b[0;31m                               run_metadata=run_metadata)\n\u001b[0m\u001b[1;32m   1044\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0m_PREEMPTION_ERRORS\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1045\u001b[0m         logging.info('An error was raised. This may be due to a preemption in '\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1132\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0moriginal_exc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1133\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1134\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0moriginal_exc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    691\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 693\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    694\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    695\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1117\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1119\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1120\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_PREEMPTION_ERRORS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1121\u001b[0m       \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1189\u001b[0m                                   \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1190\u001b[0m                                   \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1191\u001b[0;31m                                   run_metadata=run_metadata)\n\u001b[0m\u001b[1;32m   1192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1193\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    969\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 971\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun_step_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_with_hooks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    898\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 900\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    901\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1134\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1135\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1136\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1137\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1314\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1315\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1316\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1317\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1318\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1333\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1334\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1335\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1337\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor with shape[77,8,588,1638] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv1/Conv2D = Conv2D[T=DT_FLOAT, data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"VALID\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](conv1/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, conv1/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: deconv5/Shape/_241 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_214_deconv5/Shape\", tensor_type=DT_INT32, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\nCaused by op 'conv1/Conv2D', defined at:\n  File \"/usr/lib/python3.5/runpy.py\", line 184, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/usr/lib/python3.5/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 486, in start\n    self.io_loop.start()\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 127, in start\n    self.asyncio_loop.run_forever()\n  File \"/usr/lib/python3.5/asyncio/base_events.py\", line 345, in run_forever\n    self._run_once()\n  File \"/usr/lib/python3.5/asyncio/base_events.py\", line 1312, in _run_once\n    handle._run()\n  File \"/usr/lib/python3.5/asyncio/events.py\", line 125, in _run\n    self._callback(*self._args)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 117, in _handle_events\n    handler_func(fileobj, events)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/tornado/stack_context.py\", line 276, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n    self._handle_recv()\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n    callback(*args, **kwargs)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/tornado/stack_context.py\", line 276, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2903, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/home/spencer/Documents/MachineLearning/project_venv/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2963, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-2-f603c6357114>\", line 25, in <module>\n    predictions = list(classifier.predict(input_fn=predict_input_fn))\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py\", line 496, in predict\n    features, None, model_fn_lib.ModeKeys.PREDICT, self.config)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py\", line 831, in _call_model_fn\n    model_fn_results = self._model_fn(features=features, **kwargs)\n  File \"<ipython-input-1-ef488517f438>\", line 28, in cnn_model\n    strides = stridesSize, activation = tf.nn.relu, name = \"conv1\")\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/convolutional.py\", line 621, in conv2d\n    return layer.apply(inputs)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py\", line 828, in apply\n    return self.__call__(inputs, *args, **kwargs)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py\", line 717, in __call__\n    outputs = self.call(inputs, *args, **kwargs)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/convolutional.py\", line 168, in call\n    outputs = self._convolution_op(inputs, self.kernel)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py\", line 868, in __call__\n    return self.conv_op(inp, filter)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py\", line 520, in __call__\n    return self.call(inp, filter)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py\", line 204, in __call__\n    name=self.name)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_nn_ops.py\", line 956, in conv2d\n    data_format=data_format, dilations=dilations, name=name)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py\", line 3392, in create_op\n    op_def=op_def)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py\", line 1718, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[77,8,588,1638] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv1/Conv2D = Conv2D[T=DT_FLOAT, data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"VALID\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](conv1/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, conv1/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: deconv5/Shape/_241 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_214_deconv5/Shape\", tensor_type=DT_INT32, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as mpimg\n",
    "import pickle\n",
    "import tensorflow as tf\n",
    "\n",
    "train_images_raw = pickle.load(open(\"bridge.p\", \"rb\" ))\n",
    "\n",
    "classifier = tf.estimator.Estimator(\n",
    "        model_fn= cnn_model,\n",
    "        model_dir= \"./tmp/lane_prediction\")\n",
    "\n",
    "I = train_images_raw\n",
    "    \n",
    "plt.figure()\n",
    "plt.imshow(I[0])\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(I[1])\n",
    "\n",
    "\n",
    "predict_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "    x=np.array(I),\n",
    "    num_epochs=1,\n",
    "    shuffle=False)\n",
    "\n",
    "predictions = list(classifier.predict(input_fn=predict_input_fn))\n",
    "cnn_road_labels = np.array([p['predict_lane'] for p in predictions])\n",
    "pickle.dump(cnn_road_labels,open('bridge_CNN_labels.p', \"wb\" ))\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
